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Improving Oral Cancer Outcomes with Imaging and Artificial Intelligence.

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|February 21, 2020
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Early detection of oral cancer is crucial for better outcomes. Novel optical imaging and artificial intelligence (AI) approaches show promise for improving the accuracy of oral cancer detection and diagnosis, potentially impacting patient survival rates.

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Early diagnosis significantly impacts oral and oropharyngeal squamous cell carcinoma (OPSCC) outcomes, but most cases are diagnosed late, leading to poor prognoses.
  • Current screening methods by non-specialists lack accuracy due to the unreliable clinical appearance of oral lesions, resulting in poor sensitivity and specificity.
  • Existing optical imaging modalities have not achieved widespread adoption or significantly improved clinical practice for oral cancer detection.

Purpose of the Study:

  • To provide an overview of emerging optical imaging modalities and artificial intelligence (AI) approaches for oral cancer detection.
  • To evaluate the individual and combined utility of these technologies in improving oral cancer detection and patient outcomes.
  • To contextualize image-based detection principles within clinical needs and parameters.

Main Methods:

  • Review and evaluation of studies utilizing optical imaging modalities for oral cancer detection.
  • Overview and assessment of artificial intelligence (AI) algorithms and their application in medical diagnostics.
  • Analysis of research combining optical imaging and AI for oral cancer screening and diagnosis, including smartphone-based probes and optical coherence tomography (OCT).

Main Results:

  • While novel imaging modalities have been investigated, none have significantly impacted clinical practice for oral cancer.
  • AI approaches are increasingly improving diagnostic accuracy in medicine, with emerging applications in oral cancer detection.
  • Studies combining AI with imaging demonstrate considerable potential for improving oral cancer outcomes, from low-cost screening to detailed lesion analysis.

Conclusions:

  • Combined optical imaging and AI approaches offer a significant opportunity to enhance oral cancer detection and diagnosis.
  • These integrated technologies can improve patient outcomes by enabling earlier and more accurate identification of oral cancers.
  • Future applications may include AI-guided analysis of imaging data for improved screening and diagnostic accuracy.